PAXIP1-AS1 is associated with immune infiltration and predicts poor prognosis in ovarian cancer

The long non-coding RNA (LncRNA) PAXIP1 antisense RNA 1 (PAXIP1-AS1) was found to promote proliferation, migration, EMT, and apoptosis of ovarian cancer (OC) cells in OC cell lines, but the relationship between PAXIP1-AS1 expression and clinical characteristics, prognosis, and immune infiltration of OC patients and its regulatory network are unclear. 379 OC tissues were collected from The Cancer Genome Atlas (TCGA) database. 427 OC tissues and 88 normal ovarian tissues were collected from GTEx combined TCGA database. 130 OC samples were collected from GSE138866. Kruskal-Wallis test, Wilcoxon sign-rank test, logistic regression, Kaplan-Meier method, Cox regression analysis, Gene set enrichment analysis (GSEA), and immuno-infiltration analysis were used to evaluate the relationship between clinical characteristics and PAXIP1-AS1 expression, prognostic factors, and determine the significant involvement of PAXIP1-AS1 in function. QRT-PCR was used to validate the expression of PAXIP1-AS1 in OC cell lines. Low PAXIP1-AS1 expression in OC was associated with age (P = 0.045), histological grade (P = 0.011), and lymphatic invasion (P = 0.004). Low PAXIP1-AS1 expression predicted a poorer overall survival (OS) (HR: 0.71; 95% CI: 0.55–0.92; P = 0.009), progression free interval (PFS) (HR: 1.776; 95% CI: 1.067–2.955; P = 0.001) and disease specific survival (DSS) (HR: 0.67; 95% CI: 0.51–0.89; P = 0.006). PAXIP1-AS1 expression (HR: 0.711; 95% CI: 0.542–0.934; P = 0.014) was independently correlated with PFS in OC patients. GSEA demonstrated that neutrophil degranulation, signaling by Interleukins, GPCR-ligand binding, G alpha I signaling events, VEGFAVEGFR-2 signaling pathway, naba secreted factors, Class A 1 Rhodopsin-Like Receptors, PI3K-Akt signaling pathway, and Focal Adhesion-PI3K-Akt-mTOR-signaling pathway were differentially enriched in PAXIP1-AS1 high expression phenotype. PAXIP1-AS1 was significantly downregulated in OC cell lines compared with IOSE29 cell line. The expression of PAXIP1-AS1 was associated with immune infiltration. low expression of PAXIP1-AS1 was correlated with poor OS (HR: 0.52; 95% CI: 0.34–0.80; P = 0.003) from GSE138866. There were some genomic variations between the PAXIP1-AS1 high and low expression groups. Low expression of PAXIP1-AS1 was significantly associated with poor survival and immune infiltration in OC. PAXIP1-AS1 could be a promising prognosis biomarker and response to immunotherapy for OC.


Data processing
Data were RNAseq data in level 3 HTSeq-FPKM format from the TCGA (https://portal.gdc. cancer.gov/) OC project [18]. RNAseq data in fragments per million kilobases (FPKM) format were converted to transcripts per million reads (TPM) format and log2 for analysis [19,20]. Supplementary data were prognostic data from a reference [21]. The data was filtered by retaining clinical information data.

Differential expression of PAXIP1-AS1
427 OC tissues and 88 normal ovarian tissues were collected from GTEx combined TCGA database. UCSC XENA (https://xenabrowser.net/datapages/) TPM-formatted RNAseq data from TCGA and GTEx were processed uniformly by the Toil process [22]. Extracted OC data corresponding to TCGA and normal tissue data corresponding to GTEx. The data were not filtered. Data were processed as log2 (value+1).
The R package included ggplot2 [3.3.6], stats [4.2.1], and car [3.1-0]. The process was to select the appropriate statistical method for the statistics (stats package and car package) depending on the characteristics of the data format and to visualize the data using the ggplot2 package. The statistical method was the Welch t' test. The dependent variable was PAXI-P1-AS1 [ENSG00000273344]. Details of the code can be obtained from "groupplot_gene.R" (https://github.com/BuzeChen15262020735/PAXIP1-AS1.git).

The relationship between PAXIP1-AS1 and clinical characteristics
Logistic analysis. The R package was mainly the base package. The statistical method was the dichotomous logistic model. The dependent variable was PAXIP1-AS1. The type of independent variable was low high dichotomous. Details of the code can be obtained from " gene_logistic.R" (https://github.com/BuzeChen15262020735/PAXIP1-AS1.git).
We used the ggplot2 package to complete the forest plot.

QRT-PCR
The OC cell lines SKOV3 and A2780 and the human ovarian surface epithelial cell line IOSE29, which are maintained in our laboratory, were used in this study. Cells are cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillinstreptomycin. All cells are stored in a humidified incubator with 5% CO 2 at 37˚C. The PAXI-P1-AS1 levels in SKOV3, A2780, and IOSE29 cell lines were identified by qRT-PCR [28][29][30]. The primer sequences used are shown in Table 1. The thermal PCR profile was as follows: predenaturation at 94˚C for 3 minutes; denaturation at 94˚C 30 seconds, annealing at 50˚C for 40 seconds, extension at 72˚C for 1 minute, 30 cycles; extension at 72˚C for 10 minutes. ΔΔct is used to calculate qRT-PCR data. Using the same set of target gene-internal reference genes, which is the first Δct, Δct is subtracted from ΔΔct, and then we can calculate the exact value using the function = power (2, -ΔΔct).

Correlation between PAXIP1-AS1 and prognosis in GSE138866
130 OC patients were collected from GSE138866. PAXIP1-AS1 expression data in patients were used to validate the prognostic value of PAXIP1-AS1. The R packages were the survminer package [0. 4.9], and the survival package [3.2-10].

Genomic variation between PAXIP1-AS1 high and low expression groups
Patients with OC were grouped according to high or low PAXIP1-AS gene expression and their mutations were mapped oncoplot separately using the maftools package. Details of the code can be obtained from "Maftools.R" (https://github.com/BuzeChen15262020735/ PAXIP1-AS1.git).

Statistical analysis
All statistical analyses were performed using R (v.3.6.3) [31,32]. Wilcoxon rank-sum test, chisquare test, and Fisher's exact test were used to analyze the relationship between clinical features and PAXIP1-AS1. P values less than 0.05 were considered statistically significant.

Clinical characteristics
As shown in

PAXIP1-AS1 expression correlated with poor clinical characteristics of OC
PAXIP1-AS1 was low expressed in OC tissues (3.003±0.034 vs. 3.126±0.046, P = 0.032), based on 427 OC tissues and 88 normal ovarian tissues of GTEx combined TCGA database (Fig 1). The characteristics of OC patients were shown in Table 2, clinical and gene expression data were collected from TCGA database. According to the mean value of relative PAXIP1-AS1 expression, the patients with OC were divided into high (n = 190) and low (n = 189) expression groups. PAXIP1-AS1 expression was associated with age (P = 0.002), histological grade (P = 0.007), and lymphatic invasion (P = 0.007). As shown in Fig 2 and Table 3, PAXIP1-AS1 was significantly related to age (P = 0.045), histological grade (P = 0.011), and lymphatic invasion (P = 0.004).  Table 4 and Fig 4,

PAXIP1-AS1-related pathways based on GSEA
A dataset of 111 significant differences was enriched in PAXIP1-AS1 high expression phenotype. As shown in Table 5 and Fig 6, the top 9 low P-value datasets included neutrophil degranulation, signaling by interleukins, GPCR-ligand binding, G alpha I signaling events, VEGFA-VEGFR2 signaling pathway, secreted factors, Class A 1 Rhodopsin-Like Receptors, PI3K-Akt signaling pathway and focal adhesion-PI3K-Akt-mTOR-signaling pathway.

Correlation of PAXIP1-AS1 expression with immune infiltration
For aDC, the mean level of PAXIP1-AS1 in the high expression group (0.398±0.147) was significantly lower than that in the low expression group (0.436±0.123) (P = 0.006) (Fig 7A). The correlation analysis (r = -0.110, P = 0.025) showed a negative correlation between PAXIP1-AS1 and aDC (Figs 8A and 9). For B cells, the mean level of PAXIP1-AS1 in the high expression group (0.174±0.07) was significantly lower than that in the low expression group (0.195±0.07) (P = 0.004) (Fig 7B). The correlation analysis (r = -0.190, P<0.001) showed a negative correlation between PAXIP1-AS1 and B cells (Figs 8B and 9). For CD8 T cells, the mean level of PAX-IP1-AS1 in the high expression group (0.606±0.021) was significantly lower than the mean level in the low expression group (0.611±0.021) (P = 0.013) (Fig 7C). The correlation analysis

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The role of PAXIP1-AS1 in OC

Comparison of genomic variation between PAXIP1-AS1 high and low expression groups
As shown in Fig 12, the top 10 shared genes with the highest mutation frequencies included TP53, TTN, CSMD3, USH2A, MUC16, MUC17, RYR2, HMCN1, DST, and FLG in the PAXI-P1-AS1 high and low expression groups, As for CSMD3, the PAXIP1-AS1 high and low

PLOS ONE
The role of PAXIP1-AS1 in OC expression groups contained nonsense mutation and muti hit, respectively. As for USH2A, the PAXIP1-AS1 high and low expression groups contain nonsense mutation and splice site, and muti hit, respectively. As for MUC16, the PAXIP1-AS1 high expression group contains nonsense mutation compared to the PAXIP1-AS1 low expression group. As for MUC17, the PAX-IP1-AS1 low expression group contains nonsense mutation compared to the PAXIP1-AS1

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high expression group. As for RYR2, the PAXIP1-AS1 high expression group contains frame shift del compared to the PAXIP1-AS1 low expression group. As for HMCN1, the PAXI-P1-AS1 high expression group contains nonsense mutation, frame shift del and muti hit compared to the PAXIP1-AS1 low expression group. As for DST, the PAXIP1-AS1 high expression group contains frame shift ins compared to the PAXIP1-AS1 low expression group. As for FLG, the PAXIP1-AS1 low expression group contains muti hit compared to the PAXIP1-AS1 high expression group. These results may help us to explore the possible mechanisms of PAXI-P1-AS1-mediated ovarian carcinogenesis.

Discussion
LncRNAs have been implicated in the molecular mechanisms of carcinogenesis [33]. As regulators of the flow of genetic information interacting with epigenetic, transcriptional, and posttranscriptional pathways, lncRNAs promote tumor formation, progression, and metastasis in many human malignancies [34]. Understanding the specific molecular events that underpin OC tumorigenesis can lead to early detection and improved outcomes. LOXL1-AS1 expression correlates with poor clinical outcome in EOC patients and can be used as an independent prognostic indicator as well as a new diagnostic biomarker [35]. LINC00472 may be a potential tumor suppressor in OS by interacting with miR-300 and FOXO1 [36]. High plasma levels of lncRNA ROR can be used as a potential biomarker for the diagnosis of OC [37]. Therefore, it is important to study lncRNAs as new prognosis OC biomarkers and therapeutic targets in the future.
High expression of PAXIP1-AS1 was observed in OC cell lines compared with HOSEpiC cell line, and exhibited an oncogenic role by facilitating cell proliferation, migration, EMT, and suppressing cell apoptosis [14]. In this study, the expression of PAXIP1-AS1 in OC were significantly lower than that in paired normal tissues, as well as in ovarian surface epithelial cells and OC cells. OC patients (age, > 60) showed significant low PAXIP1-AS1 expression. OC patients (G3 & G4) showed significant low PAXIP1-AS1 expression. OC patients (lymphatic invasion) showed significant low PAXIP1-AS1 expression. It indicated that patients with OC of high age, high differentiation, and lymphatic invasion have a poor prognosis. Expression of PAXIP1-AS1 was positively correlated with poor OS (P = 0.009), PFS (P = 0.001), and DSS (P = 0.006) of OC patients. PAXIP1-AS1 expression (HR: 0.711; 95% CI: 0.542-0.934; P = 0.014) was an independently correlated with PFS in OC patients. In conclusion, PAXI-P1-AS1 is a good molecular marker of prognosis for patients with OC.
Immune infiltration of OC is currently a hot topic and understanding of immune infiltration will facilitate the development of immunotherapy for OC. The results of this study showed a modest relationship between PAXIP1-AS1 expression and immune cells in OC. These correlations may suggest that PAXIP1-AS1 may inhibit the function of aDC, B cells, CD8 T cells, Cytotoxic cells, DC, iDC, Macrophages, Mast cells, Neutrophils, NK CD56dim cells, T cells, TFH, Tgd, Th1 cells, Th2 cells and Treg, which in turn exert a suppressive effect on OC through a potential mechanism. Differences in genomic variation between high and low PAX-IP1-AS1 expression groups need to be further investigated for the mechanism of PAXI-P1-AS1-mediated ovarian carcinogenesis.
Although there are some limitations, this is the further study to explore the relationship between PAXIP1-AS1 and OC. This study was mainly based on bioinformatic analysis and could be further strengthened by experimental studies. The mechanism of PAXIP1-AS1-mediated ovarian carcinogenesis needs to be further investigated. Since there are some contradictory findings between this study and others, additional sample specimens, external validation cohorts, and specific biological experiments should be developed to improve the reliability. The innovation and reliability of this study needs to be improved.

Conclusions
PAXIP1-AS1 showed low expression in OC tissues and cell lines. Low expression of PAXI-P1-AS1 was related to poor OS, PFS, and DSS in OC patients. PAXIP1-AS1 might participate in the development of OC by pathways including neutrophil degranulation, signaling by interleukins, GPCR-ligand binding, G alpha I signaling events, VEGFA-VEGFR2 signaling pathway, secreted factors, Class A 1 Rhodopsin-Like Receptors, PI3K-Akt signaling pathway, and focal adhesion-PI3K-Akt-mTOR-signaling pathway. PAXIP1-AS1 expression was associated with immune infiltrating cells. There were some genomic variations between the PAXIP1-AS1 high and low expression groups. This study partly revealed that PAXIP1-AS1 could be a promising prognosis biomarker and response to immunotherapy for OC.